A Parallel Ensemble Learning Model for Fault Detection and Diagnosis of Industrial Machinery

Al-Andoli, Mohammed Nasser; Tan, Shing Chiang; Sim, Kok Swee; Seera, Manjeevan; Lim, Chee Peng

doi:10.1109/access.2023.3267089

Cited by 6 publications

(2 citation statements)

References 43 publications

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“…Methods that perform the time-frequency analysis, such as the Short-Time Fourier Transform (STFT) or Continuous Wavelet Transform (CWT) [18], are also attractive in the AC motors stator winding fault diagnosis field. Automation of the AC motors stator winding fault detection and classification process in recent years has most often been implemented using a variety of artificial intelligence techniques [19], such as machine learning algorithms [20,21] and deep learning (DL) [22]. When it comes to computerized diagnostic systems, the fastest growing area in recent years is the application of DL, especially convolutional neural networks (CNN).…”

Section: Introductionmentioning

confidence: 99%

Microcontroller-Based Embedded System for the Diagnosis of Stator Winding Faults and Unbalanced Supply Voltage of the Induction Motors

Pietrzak,

Wolkiewicz

2024

Energies

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Induction motors (IMs) are one of the most widely used motor types in the industry due to their low cost, high reliability, and efficiency. Nevertheless, like other types of AC motors, they are prone to various faults. In this article, a low-cost embedded system based on a microcontroller with the ARM Cortex-M4 core is proposed for the extraction of stator winding faults (interturn short circuits) and an unbalanced supply voltage of the induction motor drive. The voltage induced in the measurement coil by the axial flux was used as a source of diagnostic information. The process of signal measurement, acquisition, and processing using a cost-optimized embedded system (NUCLEO-L476RG), with the potential for industrial deployment, is described in detail. In addition, the analysis of the possibility of distinguishing between interturn short circuits and unbalanced supply voltage was carried out. The effect of motor operating conditions and fault severity on the symptom extraction process was also studied. The results of the experimental research conducted on a 1.5 kW IM confirmed the effectiveness of the developed embedded system in the extraction of these types of faults.

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Section: Introductionmentioning

confidence: 99%

Microcontroller-Based Embedded System for the Diagnosis of Stator Winding Faults and Unbalanced Supply Voltage of the Induction Motors

Pietrzak,

Wolkiewicz

2024

Energies

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“…Deep learning (DL) models have shown great success in a wide range of applications, such as image classification [1] and segmentation [2], malware detection [3,4], object detection and tracking [5], fault detection [6], speech recognition [7], and complex network analysis [8,9]. The high accuracy performance of DL models is attributed to their continuous development, availability of data, and increase in computational power.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Robust Deep Learning Models Against Adversarial Attacks Based on a Protection Layer Approach

Al-Andoli,

Tan,

Sim

et al. 2024

IEEE Access

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Deep learning (DL) has demonstrated remarkable achievements in various fields. Nevertheless, DL models encounter significant challenges in detecting and defending against adversarial samples (AEs). These AEs are meticulously crafted by adversaries, introducing imperceptible perturbations to clean data to deceive DL models. Consequently, AEs pose potential risks to DL applications. In this paper, we propose an effective framework for enhancing the robustness of DL models against adversarial attacks. The framework leverages convolutional neural networks (CNNs) for feature learning, Deep Neural Networks (DNNs) with softmax for classification, and a defense mechanism to identify and exclude AEs. Evasion attacks are employed to create AEs to evade and mislead the classifier by generating malicious samples during the test phase of DL models i.e., CNN and DNN, using the Fast Gradient Sign Method (FGSM), Basic Iterative Method (BIM), Projected Gradient Descent (PGD), and Square Attack (SA). A protection layer is developed as a detection mechanism placed before the DNN classifier to identify and exclude AEs. The detection mechanism incorporates a machine learning model, which includes one of the following: Fuzzy ARTMAP, Random Forest, K-Nearest Neighbors, XGBoost, or Gradient Boosting Machine. Extensive evaluations are conducted on the MNIST, CIFAR-10, SVHN, and Fashion-MNIST data sets to assess the effectiveness of the proposed framework. The experimental results indicate the framework's ability to effectively and accurately detect AEs generated by four popular attacking methods, highlighting the potential of our developed framework in enhancing its robustness against AEs.

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An improved ensemble learning model-based strategy for fault diagnosis of lithium battery double roller press equipment

Xiao,

Song,

Yin

et al. 2024

Meas. Sci. Technol.

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The production process of lithium batteries is intricate, involving the coordination of various types of equipment.The sta-bility and precision of double roller press equipment directly affect product performance. With the increasing global de-mand for green energy, the application of lithium batteries in electric vehicles and energy storage systems is expanding, which imposes higher requirements on the stability and quality of lithium battery production. It is an important topic to address the challenges brought about by the gradual intelligentization of double roller presses, such as the complexifica-tion of control systems and the diversification of fault reasons. This paper proposes an enhanced ensemble learning model-based fault diagnosis strategy for lithium battery double roller press equipment. Firstly, the K-nearest neighbors (KNN) algorithm is employed to handle missing data, combined with normalization and standardization methods to improve fea-ture processing, thereby enhancing data quality. Secondly, the Maximum Information Coefficient (MIC) algorithm is utilized to select features highly correlated with fault labels, combined with the Recursive Feature Elimination with Cross-Validation (RFECV) to further optimize feature selection, creating an optimal feature subset. Finally, a RXS-XGBoost model is constructed through the Stacking ensemble learning method, selecting Random Forest (RF), XGBoost, and Sup-port Vector Machines (SVM) as base learners, with XGBoost as the meta-learner. This ensemble approach aims to lever-age the complementary advantages of different algorithms, enhancing the accuracy and robustness of fault diagnosis. The experimental results demonstrate that this improved ensemble learning diagnostic strategy achieves an accuracy rate of up to 99.05%, which is significantly better than other fault diagnosis strategies. It not only effectively reduces the model's training complexity and the risk of overfitting but also significantly enhances the efficiency and precision of fault diagno-sis for lithium battery double roller press equipment.

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A Parallel Ensemble Learning Model for Fault Detection and Diagnosis of Industrial Machinery

Cited by 6 publications

References 43 publications

Microcontroller-Based Embedded System for the Diagnosis of Stator Winding Faults and Unbalanced Supply Voltage of the Induction Motors

Microcontroller-Based Embedded System for the Diagnosis of Stator Winding Faults and Unbalanced Supply Voltage of the Induction Motors

A Framework for Robust Deep Learning Models Against Adversarial Attacks Based on a Protection Layer Approach

An improved ensemble learning model-based strategy for fault diagnosis of lithium battery double roller press equipment

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